【摘要】 网络安全态势感知NSSA(Network Security Situation Awareness)是目前网络安全领域的热点研究内容,开展NSSA的研究,对提高我国的网络安全水平有着重要的意义。本文提出了一个NSSA模型,利用多层前馈神经网络,对采集的多个异质的传感器数据进行了融合。为提高融合的实时性,本文还设计了简单易行的特征约简方法,大大降低了融合引擎的输入维数。最后,本文利用安全态势生成算法,对网络安全事件进行了加权量化。实验表明,本文所提出的模型和方法是可行的和有效的。更多还原

【Abstract】 Network Security Situation Awareness(NSSA) is a hot research spot in the area of network security and it is significant to study NSSA in order to improve the security level of our nation.This paper presents a NSSA model based on data fusion.The NSSA model employs multi-layer feedforward neural network as its fusion engine and fuses the data provided by the sensors in an intelligent and efficient manner.Furthermore,this paper discusses a network security situation generation agorithm which expresses the security situation by the weighted quantization of security events.In addition,it also designs a feature reduction method in order to improve the real-time nature of the NSSA.Our model and approach are proved to be feasible and effective through a series experiments using real network traffic.更多还原